# Setting the path
setwd("C:/Users/lulut/Desktop/RRproject436819")
today <- Sys.Date()
year_ago <- as.Date(today - 365)
#Create a vector containing WIG20 company stocks, specifying the time range of the data, from last year to today this year
company_list <- c("ALR.WA", "ACP.WA", "PEO.WA", "CDR.WA", "CPS.WA",
"DNP.WA", "GTN.WA", "JSW.WA", "KGH.WA", "KRU.WA",
"LPP.WA", "MBK.WA", "OPL.WA", "PKO.WA", "PGE.WA",
"PKN.WA", "SPL.WA", "TPE.WA", "PZU.WA", "CCC.WA")
#Load data from Yahoo Finance, and save it to an RDATA file
library(quantmod)
for (company in company_list) {
assign(company, getSymbols(company, src = 'yahoo', from = year_ago, to = today, auto.assign = FALSE))
}
save.image(file = "Data.RData")
print(ACP.WA)
library(dplyr)
library(corrplot)
library(ggplot2)
library(GGally)
library(xts)
library(MASS)
library(gridExtra)
```{r}
Error: attempt to use zero-length variable name
str(ALR.WA)
An xts object on 2023-06-12 / 2024-06-10 containing:
Data: double [251, 6]
Columns: ALR.WA.Open, ALR.WA.High, ALR.WA.Low, ALR.WA.Close, ALR.WA.Volume ... with 1 more column
Index: Date [251] (TZ: "UTC")
xts Attributes:
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2024-06-11 07:24:01"
#From the line chart, we can see that except for Cyfrowy Polsat and CD Project, the stock prices of other companies have been relatively stable since June 2023.
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiRXJyb3I6IGF0dGVtcHQgdG8gdXNlIHplcm8tbGVuZ3RoIHZhcmlhYmxlIG5hbWVcbiJ9 -->
Error: attempt to use zero-length variable name
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZm9yKGkgaW4gMToyMCl7XG4gIHByaW50KGhlYWQoZXZhbChwYXJzZSh0ZXh0ID1jb21wYW55X2xpc3RbaV0pKSwgNSkpXG59XG5cbmBgYCJ9 -->
```r
for(i in 1:20){
print(head(eval(parse(text =company_list[i])), 5))
}
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 45.99 45.99 44.44 44.44 142929 42.50953 NA NA NA
2023-06-13 44.48 44.95 43.30 43.40 150186 41.51471 NA NA NA
2023-06-14 43.30 46.10 43.30 45.22 345980 43.25565 NA NA NA
2023-06-15 45.00 45.85 43.71 45.45 293856 43.47566 NA NA NA
2023-06-16 45.65 47.01 44.00 45.92 613946 43.92524 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 84.5 84.80 83.20 83.20 89477 79.73333 NA NA NA
2023-06-13 84.0 84.65 82.70 82.85 73472 79.39791 NA NA NA
2023-06-14 83.4 84.60 82.30 83.85 99587 80.35625 NA NA NA
2023-06-15 84.1 84.65 83.05 84.00 132609 80.50000 NA NA NA
2023-06-16 80.6 84.20 80.60 81.65 427038 81.65000 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 108.70 110.70 107.60 108.00 789495 92.31525 NA NA NA
2023-06-13 108.50 109.10 106.20 106.90 406602 91.37501 NA NA NA
2023-06-14 107.70 112.35 107.65 112.00 1055785 95.73433 NA NA NA
2023-06-15 111.70 112.45 109.05 112.05 768988 95.77708 NA NA NA
2023-06-16 112.05 114.20 111.10 112.10 1262988 95.81981 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 133.00 143.5 132.95 141.40 1584134 141.40 NA NA NA
2023-06-13 142.75 143.1 138.60 140.30 440282 140.30 NA NA NA
2023-06-14 140.45 160.0 140.30 159.00 1352766 159.00 NA NA NA
2023-06-15 159.90 168.4 155.25 168.25 1428362 168.25 NA NA NA
2023-06-16 168.25 173.0 159.80 170.00 1616168 170.00 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 16.515 16.765 16.260 16.390 402615 16.390 NA NA NA
2023-06-13 16.560 16.660 16.275 16.320 630782 16.320 NA NA NA
2023-06-14 16.360 17.210 16.360 17.035 631292 17.035 NA NA NA
2023-06-15 17.000 17.375 16.850 17.140 571303 17.140 NA NA NA
2023-06-16 17.100 17.645 16.880 17.045 2571753 17.045 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 460.4 473.2 460.4 469.9 332983 469.9 NA NA NA
2023-06-13 475.1 479.7 470.7 476.5 182186 476.5 NA NA NA
2023-06-14 477.7 478.3 470.2 473.5 208677 473.5 NA NA NA
2023-06-15 474.7 491.8 470.3 484.1 250572 484.1 NA NA NA
2023-06-16 485.0 491.2 474.1 477.1 515428 477.1 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 0.4165 0.4165 0.4110 0.4120 143559 0.2779759 NA NA NA
2023-06-13 0.4110 0.4150 0.4110 0.4150 138775 0.2800000 NA NA NA
2023-06-14 0.4110 0.4160 0.4105 0.4110 440792 0.2773012 NA NA NA
2023-06-15 0.4110 0.4200 0.4100 0.4195 120458 0.2830361 NA NA NA
2023-06-16 0.4135 0.4250 0.4120 0.4160 168188 0.2806747 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 42.80 42.80 41.20 41.45 313567 41.45 NA NA NA
2023-06-13 41.95 42.66 40.91 42.06 422858 42.06 NA NA NA
2023-06-14 42.08 44.94 42.06 44.31 921100 44.31 NA NA NA
2023-06-15 44.70 44.84 43.91 44.36 423611 44.36 NA NA NA
2023-06-16 44.70 46.52 41.80 42.34 2078951 42.34 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 113.8 114.35 110.80 110.90 879699 109.9984 NA NA NA
2023-06-13 113.1 115.15 112.75 114.30 862881 113.3707 NA NA NA
2023-06-14 114.0 119.70 113.55 118.95 1493011 117.9829 NA NA NA
2023-06-15 118.2 120.85 117.45 120.75 763033 119.7683 NA NA NA
2023-06-16 121.0 122.55 119.50 120.70 1263656 119.7187 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 384.6 395.2 383.0 384.4 27303 355.7658 NA NA NA
2023-06-13 386.2 394.4 377.6 381.4 37594 352.9893 NA NA NA
2023-06-14 385.0 392.0 375.8 384.4 53683 355.7658 NA NA NA
2023-06-15 390.8 390.8 378.0 382.6 83963 354.0999 NA NA NA
2023-06-16 382.4 388.8 380.8 386.0 108703 357.2466 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 13790 14700 13610 14500 5760 13778.33 NA NA NA
2023-06-13 14570 14910 13900 13900 8376 13208.20 NA NA NA
2023-06-14 14200 14350 13850 14090 6901 13388.74 NA NA NA
2023-06-15 13800 14290 13500 14150 4742 13445.75 NA NA NA
2023-06-16 14290 14650 14150 14360 8705 13645.30 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 395.0 396.8 378.6 378.9 37959 378.9 NA NA NA
2023-06-13 378.9 384.0 371.9 375.8 41269 375.8 NA NA NA
2023-06-14 377.0 396.7 376.0 394.2 40751 394.2 NA NA NA
2023-06-15 390.0 397.1 376.2 396.4 77075 396.4 NA NA NA
2023-06-16 392.8 408.0 390.0 404.4 110849 404.4 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 7.300 7.366 7.226 7.282 1616653 6.928995 NA NA NA
2023-06-13 7.372 7.380 7.120 7.134 1661326 6.788169 NA NA NA
2023-06-14 7.136 7.264 7.054 7.172 2001385 6.824327 NA NA NA
2023-06-15 7.200 7.286 7.110 7.246 1711985 6.894740 NA NA NA
2023-06-16 7.280 7.446 7.200 7.200 4889581 6.850969 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 35.79 36.01 35.28 35.34 1863176 34.42837 NA NA NA
2023-06-13 35.40 35.65 34.73 34.95 1996685 34.04843 NA NA NA
2023-06-14 34.92 35.98 34.92 35.60 3663641 34.68166 NA NA NA
2023-06-15 35.20 35.67 34.21 35.67 3958683 34.74985 NA NA NA
2023-06-16 35.67 36.30 35.43 36.17 6950418 35.23695 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 7.198 7.278 7.072 7.150 2739216 7.150 NA NA NA
2023-06-13 7.150 7.210 7.066 7.080 2317086 7.080 NA NA NA
2023-06-14 7.130 7.350 7.114 7.284 3122703 7.284 NA NA NA
2023-06-15 7.280 7.490 7.220 7.440 3420994 7.440 NA NA NA
2023-06-16 7.440 7.550 7.392 7.518 14891114 7.518 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 65.30 65.48 63.70 63.94 2209624 58.88073 NA NA NA
2023-06-13 63.99 64.50 63.80 63.94 1591950 58.88073 NA NA NA
2023-06-14 64.00 65.63 64.00 64.96 2625791 59.82002 NA NA NA
2023-06-15 65.00 65.60 64.21 65.55 2530071 60.36334 NA NA NA
2023-06-16 65.59 67.00 65.36 66.82 6534021 61.53285 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 383.0 391.2 375.2 377.0 65668 330.7738 NA NA NA
2023-06-13 378.0 378.6 368.6 370.4 89113 324.9831 NA NA NA
2023-06-14 367.0 389.2 367.0 386.4 83546 339.0212 NA NA NA
2023-06-15 382.0 393.8 378.0 390.4 90574 342.5307 NA NA NA
2023-06-16 389.8 405.6 385.2 389.2 485822 341.4779 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 2.414 2.429 2.390 2.390 1569167 2.390 NA NA NA
2023-06-13 2.400 2.405 2.342 2.354 2520116 2.354 NA NA NA
2023-06-14 2.351 2.426 2.351 2.419 2691713 2.419 NA NA NA
2023-06-15 2.408 2.490 2.360 2.480 3684935 2.480 NA NA NA
2023-06-16 2.486 2.542 2.461 2.487 5401818 2.487 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 40.51 41.21 40.32 40.49 915579 38.14162 NA NA NA
2023-06-13 40.49 40.84 40.09 40.30 1551882 37.96264 NA NA NA
2023-06-14 40.30 41.18 40.30 40.71 1890022 38.34886 NA NA NA
2023-06-15 40.52 40.88 40.30 40.82 1377421 38.45248 NA NA NA
2023-06-16 40.77 41.42 40.66 41.09 4566586 38.70682 NA NA NA
Open High Low Close Volume Adjusted MA10 MA20 MA30
2023-06-12 45.68 46.26 44.98 45.21 92317 45.21 NA NA NA
2023-06-13 45.27 45.96 44.90 45.75 47517 45.75 NA NA NA
2023-06-14 45.80 48.09 43.10 43.23 456163 43.23 NA NA NA
2023-06-15 43.19 44.08 42.21 42.94 247154 42.94 NA NA NA
2023-06-16 43.00 43.80 42.70 43.00 328555 43.00 NA NA NA
for (i in 1:length(company_list)) {
print(plot_graphic(eval(parse(text =company_list[i])), company_list[i]))
}
stock_returns <- list()
for(i in 1:20){
stock_returns[[i]] <- eval(parse(text =company_list[i]))$`Daily Returns`
}
stock_returns <- do.call(cbind,stock_returns)
colnames(stock_returns) <- company_list
head(stock_returns)
ALR.WA ACP.WA PEO.WA CDR.WA CPS.WA DNP.WA GTN.WA JSW.WA
2023-06-12 NA NA NA NA NA NA NA NA
2023-06-13 -0.023402272 -0.004206756 -0.0101851703 -0.007779285 -0.004270878 0.014045555 0.007281510 0.014716540
2023-06-14 0.041935496 0.012070039 0.0477080299 0.133285791 0.043811285 -0.006295908 -0.009638498 0.053495005
2023-06-15 0.005086245 0.001788947 0.0004465221 0.058176101 0.006163754 0.022386497 0.020681304 0.001128396
2023-06-16 0.010340984 0.014285733 0.0004461635 0.010401189 -0.005542551 -0.014459822 -0.008343269 -0.045536529
2023-06-19 0.003266596 0.003061849 -0.0115967506 -0.024705864 0.006746833 -0.019283194 0.009615327 -0.026924879
KGH.WA KRU.WA LPP.WA MBK.WA OPL.WA PKO.WA PGE.WA PKN.WA
2023-06-12 NA NA NA NA NA NA NA NA
2023-06-13 0.0306582732 -0.007804261 -0.04137936 -0.008181594 -0.020324124 -0.011035668 -0.009790234 0.000000000
2023-06-14 0.0406823705 0.007865647 0.01366909 0.048962280 0.005326633 0.018597984 0.028813559 0.015952402
2023-06-15 0.0151324428 -0.004682471 0.00425833 0.005580877 0.010317887 0.001966213 0.021416823 0.009082573
2023-06-16 -0.0004141221 0.008886468 0.01484096 0.020181635 -0.006348363 0.014017398 0.010483880 0.019374506
2023-06-19 -0.0294117253 -0.008808195 -0.01949861 -0.018793289 0.001666675 -0.006911759 -0.001330171 -0.009278715
SPL.WA TPE.WA PZU.WA CCC.WA
2023-06-12 NA NA NA NA
2023-06-13 -0.017506649 -0.015062766 -0.004692558 0.011944281
2023-06-14 0.043196550 0.027612496 0.010173661 -0.055081977
2023-06-15 0.010351923 0.025217078 0.002702100 -0.006708326
2023-06-16 -0.003073669 0.002822568 0.006614427 0.001397331
2023-06-19 -0.011305372 0.040611207 -0.010221488 -0.010697653
#We can see that there is a positive correlation between some stocks
# Print mean income and covariance matrix
print(mean_income)
ALR.WA ACP.WA PEO.WA CDR.WA CPS.WA DNP.WA GTN.WA JSW.WA
3.315790e-03 3.498311e-04 2.282558e-03 8.899003e-05 -8.100618e-04 -4.560923e-04 2.600532e-03 -1.360533e-03
KGH.WA KRU.WA LPP.WA MBK.WA OPL.WA PKO.WA PGE.WA PKN.WA
1.318866e-03 1.049862e-03 1.604105e-03 2.053065e-03 6.785544e-04 2.150445e-03 1.065876e-04 4.379165e-04
SPL.WA TPE.WA PZU.WA CCC.WA
1.761037e-03 2.392414e-03 1.068477e-03 4.994033e-03
print(cov_returns)
ALR.WA ACP.WA PEO.WA CDR.WA CPS.WA DNP.WA GTN.WA JSW.WA
ALR.WA 4.632698e-04 1.411086e-04 3.041012e-04 9.241539e-05 1.201875e-04 8.245376e-05 2.540982e-05 1.648018e-04
ACP.WA 1.411086e-04 2.418422e-04 1.082367e-04 1.252965e-04 1.108472e-04 6.917301e-05 7.888457e-05 9.554207e-05
PEO.WA 3.041012e-04 1.082367e-04 4.001193e-04 9.631651e-05 8.962659e-05 6.701975e-05 2.574160e-05 1.657594e-04
CDR.WA 9.241539e-05 1.252965e-04 9.631651e-05 6.121047e-04 2.320341e-04 7.760095e-05 1.094793e-04 1.948184e-04
CPS.WA 1.201875e-04 1.108472e-04 8.962659e-05 2.320341e-04 6.155807e-04 1.035353e-04 6.687939e-05 1.371149e-04
DNP.WA 8.245376e-05 6.917301e-05 6.701975e-05 7.760095e-05 1.035353e-04 4.473522e-04 7.552152e-05 8.504483e-05
GTN.WA 2.540982e-05 7.888457e-05 2.574160e-05 1.094793e-04 6.687939e-05 7.552152e-05 1.204979e-03 4.147485e-05
JSW.WA 1.648018e-04 9.554207e-05 1.657594e-04 1.948184e-04 1.371149e-04 8.504483e-05 4.147485e-05 7.648404e-04
KGH.WA 1.753666e-04 9.849887e-05 1.598623e-04 1.534280e-04 1.318505e-04 3.897797e-05 4.505593e-05 3.035341e-04
KRU.WA 1.647793e-04 1.066880e-04 1.325563e-04 1.032739e-04 1.379589e-04 5.515122e-05 2.698117e-05 1.378331e-04
LPP.WA 1.552109e-04 1.158146e-04 1.741807e-04 1.411455e-04 1.217266e-04 9.523469e-05 1.087515e-05 7.610874e-05
MBK.WA 3.292176e-04 1.125738e-04 3.036362e-04 8.391820e-05 1.281244e-04 8.824657e-05 6.584800e-05 1.459365e-04
OPL.WA 8.252421e-05 5.584169e-05 5.660668e-05 7.291580e-05 1.180599e-04 4.798228e-05 3.505205e-05 8.804304e-05
PKO.WA 2.648124e-04 1.037098e-04 3.246279e-04 7.510950e-05 7.823997e-05 7.889308e-05 5.856858e-06 1.407797e-04
PGE.WA 1.157891e-04 9.098111e-05 9.023323e-05 2.225008e-04 2.006513e-04 9.542836e-05 1.338857e-04 1.666727e-04
PKN.WA 1.099806e-04 4.109914e-05 1.311488e-04 1.279305e-04 1.007420e-04 6.200811e-05 4.744268e-05 1.809655e-04
SPL.WA 2.562611e-04 1.195819e-04 2.829829e-04 1.033288e-04 1.058718e-04 9.870189e-05 2.821516e-05 1.598231e-04
TPE.WA 9.980538e-05 7.617087e-05 6.322717e-05 1.764132e-04 1.474643e-04 7.723825e-05 7.392630e-05 1.369531e-04
PZU.WA 1.864738e-04 1.107291e-04 1.993709e-04 6.900362e-05 1.025080e-04 8.081504e-05 4.807977e-05 1.197507e-04
CCC.WA 7.584748e-05 6.556360e-05 9.726934e-05 9.659387e-05 1.917391e-04 1.040556e-04 8.527128e-05 3.301765e-05
KGH.WA KRU.WA LPP.WA MBK.WA OPL.WA PKO.WA PGE.WA PKN.WA
ALR.WA 1.753666e-04 1.647793e-04 1.552109e-04 3.292176e-04 8.252421e-05 2.648124e-04 1.157891e-04 1.099806e-04
ACP.WA 9.849887e-05 1.066880e-04 1.158146e-04 1.125738e-04 5.584169e-05 1.037098e-04 9.098111e-05 4.109914e-05
PEO.WA 1.598623e-04 1.325563e-04 1.741807e-04 3.036362e-04 5.660668e-05 3.246279e-04 9.023323e-05 1.311488e-04
CDR.WA 1.534280e-04 1.032739e-04 1.411455e-04 8.391820e-05 7.291580e-05 7.510950e-05 2.225008e-04 1.279305e-04
CPS.WA 1.318505e-04 1.379589e-04 1.217266e-04 1.281244e-04 1.180599e-04 7.823997e-05 2.006513e-04 1.007420e-04
DNP.WA 3.897797e-05 5.515122e-05 9.523469e-05 8.824657e-05 4.798228e-05 7.889308e-05 9.542836e-05 6.200811e-05
GTN.WA 4.505593e-05 2.698117e-05 1.087515e-05 6.584800e-05 3.505205e-05 5.856858e-06 1.338857e-04 4.744268e-05
JSW.WA 3.035341e-04 1.378331e-04 7.610874e-05 1.459365e-04 8.804304e-05 1.407797e-04 1.666727e-04 1.809655e-04
KGH.WA 4.724025e-04 1.271396e-04 9.673356e-05 1.473928e-04 4.824955e-05 1.485077e-04 1.354303e-04 1.766838e-04
KRU.WA 1.271396e-04 3.722808e-04 6.041307e-05 1.305119e-04 8.991068e-05 1.116951e-04 1.375645e-04 6.619190e-05
LPP.WA 9.673356e-05 6.041307e-05 1.200626e-03 1.762740e-04 6.325195e-05 1.840822e-04 1.077532e-04 9.433152e-05
MBK.WA 1.473928e-04 1.305119e-04 1.762740e-04 4.010354e-04 7.834343e-05 2.708113e-04 1.233500e-04 1.216228e-04
OPL.WA 4.824955e-05 8.991068e-05 6.325195e-05 7.834343e-05 1.556466e-04 5.895355e-05 8.123829e-05 4.933866e-05
PKO.WA 1.485077e-04 1.116951e-04 1.840822e-04 2.708113e-04 5.895355e-05 3.340412e-04 8.722519e-05 1.240856e-04
PGE.WA 1.354303e-04 1.375645e-04 1.077532e-04 1.233500e-04 8.123829e-05 8.722519e-05 6.067869e-04 1.489913e-04
PKN.WA 1.766838e-04 6.619190e-05 9.433152e-05 1.216228e-04 4.933866e-05 1.240856e-04 1.489913e-04 2.977928e-04
SPL.WA 1.543933e-04 1.145332e-04 1.691765e-04 2.761784e-04 6.374661e-05 2.611613e-04 1.186679e-04 1.103037e-04
TPE.WA 1.160442e-04 1.200565e-04 6.845224e-05 9.403145e-05 6.265987e-05 7.454977e-05 5.157245e-04 1.119402e-04
PZU.WA 1.300344e-04 1.141547e-04 1.432462e-04 1.794112e-04 6.466498e-05 1.800729e-04 9.044664e-05 1.039322e-04
CCC.WA 1.201510e-04 1.090483e-04 3.042967e-04 9.241360e-05 6.135382e-05 7.533782e-05 1.456842e-04 8.934893e-05
SPL.WA TPE.WA PZU.WA CCC.WA
ALR.WA 2.562611e-04 9.980538e-05 1.864738e-04 7.584748e-05
ACP.WA 1.195819e-04 7.617087e-05 1.107291e-04 6.556360e-05
PEO.WA 2.829829e-04 6.322717e-05 1.993709e-04 9.726934e-05
CDR.WA 1.033288e-04 1.764132e-04 6.900362e-05 9.659387e-05
CPS.WA 1.058718e-04 1.474643e-04 1.025080e-04 1.917391e-04
DNP.WA 9.870189e-05 7.723825e-05 8.081504e-05 1.040556e-04
GTN.WA 2.821516e-05 7.392630e-05 4.807977e-05 8.527128e-05
JSW.WA 1.598231e-04 1.369531e-04 1.197507e-04 3.301765e-05
KGH.WA 1.543933e-04 1.160442e-04 1.300344e-04 1.201510e-04
KRU.WA 1.145332e-04 1.200565e-04 1.141547e-04 1.090483e-04
LPP.WA 1.691765e-04 6.845224e-05 1.432462e-04 3.042967e-04
MBK.WA 2.761784e-04 9.403145e-05 1.794112e-04 9.241360e-05
OPL.WA 6.374661e-05 6.265987e-05 6.466498e-05 6.135382e-05
PKO.WA 2.611613e-04 7.454977e-05 1.800729e-04 7.533782e-05
PGE.WA 1.186679e-04 5.157245e-04 9.044664e-05 1.456842e-04
PKN.WA 1.103037e-04 1.119402e-04 1.039322e-04 8.934893e-05
SPL.WA 3.324200e-04 9.307663e-05 1.657195e-04 9.320786e-05
TPE.WA 9.307663e-05 7.919683e-04 7.646022e-05 1.602974e-04
PZU.WA 1.657195e-04 7.646022e-05 2.458722e-04 6.092239e-05
CCC.WA 9.320786e-05 1.602974e-04 6.092239e-05 9.976232e-04
```{r}
Error: attempt to use zero-length variable name
#Show the weights of each stock in best portfolio
best_port <- portfolio[MaxSharpRatio,]
for (i in 1:length(company_list)) {
print(paste(company_list[i], ": ", best_port[i]))
}
[1] "ALR.WA : 0.135205560764959"
[1] "ACP.WA : 0.0770732023297311"
[1] "PEO.WA : 0.0294001971102957"
[1] "CDR.WA : 0.00826141606964576"
[1] "CPS.WA : 0.00941234701113828"
[1] "DNP.WA : 0.0113660008907634"
[1] "GTN.WA : 0.0686383252529199"
[1] "JSW.WA : 0.024027113403397"
[1] "KGH.WA : 0.0413148438023202"
[1] "KRU.WA : 0.0305522093428451"
[1] "LPP.WA : 0.00703220563722217"
[1] "MBK.WA : 0.054254912425709"
[1] "OPL.WA : 0.0342906330961425"
[1] "PKO.WA : 0.0738136107249253"
[1] "PGE.WA : 0.0211679835613602"
[1] "PKN.WA : 0.00496767427270371"
[1] "SPL.WA : 0.00399087231244983"
[1] "TPE.WA : 0.00617255656320359"
[1] "PZU.WA : 0.00284733701672747"
[1] "CCC.WA : 0.356210998411541"
for(j in 1:20){
start_price <- tail(eval(parse(text =company_list[j]))$Adjusted,1)
result <- monte_carlo(start_price, days, mu[company_list[j]], sigma[company_list[j]])
sim[1] <- result[days]
plot(result, type = "l", xlab = "Days", ylab = "Price", col = 1,main = paste0("Monte Carlo analysis for ", company_list[j]),ylim = c(0.88*start_price,1.12*start_price))
for (i in 2:1000) {
result <- monte_carlo(start_price, days, mu[company_list[j]], sigma[company_list[j]])
sim[i] <- result[days]
lines(result, col = i)
}
hist(sim, breaks = 100, main = "Histogram of Monte Carlo Simulations", xlab = "Price")
text(0.6, 0.7, paste("Mean: ", mean(sim), "\nStd: ", sd(sim), "\nStart Price: ", start_price))
}
for(j in 1:20){
start_price <- tail(eval(parse(text =company_list[j]))$Adjusted,1)
result <- monte_carlo(start_price, days, mu[company_list[j]], sigma[company_list[j]])
sim[1] <- result[days]
plot(result, type = "l", xlab = "Days", ylab = "Price", col = 1,main = paste0("Monte Carlo analysis for ", company_list[j]),ylim = c(0.88*start_price,1.12*start_price))
for (i in 2:1000) {
result <- monte_carlo(start_price, days, mu[company_list[j]], sigma[company_list[j]])
sim[i] <- result[days]
lines(result, col = i)
}
hist(sim, breaks = 100, main = "Histogram of Monte Carlo Simulations", xlab = "Price")
text(0.6, 0.7, paste("Mean: ", mean(sim), "\nStd: ", sd(sim), "\nStart Price: ", start_price))
}